The present invention relates generally to a field of image compression and decompression, and more particularly to a technique for rapidly compressing and decompressing image data by dividing image data streams into subregions for optimal compression of each of the subregions
Various techniques have been proposed and are currently in use for analyzing and compressing large data files, such as image data files. Image data files typically include streams of data descriptive of image characteristics, typically of intensities or other characteristics of individual pixel elements or pixels in a reconstructed image. In the medical field, for example, large image files are typically created during an image acquisition or encoding sequence, such as in an x-ray system, a magnetic resonance imaging system, a computed tomography imaging system, and so forth. The image data is then processed, such as to adjust dynamic ranges, enhance certain features shown in the image, and so forth, for storage, transmittal and display.
While image files may be stored in raw and processed formats, many image files are quite large, and would occupy considerable memory space. The increasing complexity of imaging systems also has led to the creation of very large image files, typically including more data as a result of the useful dynamic range of the imaging system and the size of the matrix of image pixels.
In addition to occupying large segments of available memory, large image files can be difficult or time consuming to transmit from one location to another. In a typical medical imaging application, for example, a scanner or other imaging device will typically create raw data which may be at least partially processed at the scanner. The data is then transmitted to other image processing circuitry, typically including a programmed computer, where the image data is further processed and enhanced. Ultimately, the image data is stored either locally at the system, or in a picture archiving and communications system (PACS) for later retrieval and analysis. In all of these data transmission steps, the large image data file must be accessed and transmitted from one device to another.
Conventional compression and decompression techniques have offered various improvements to reduce compression and decompression times, and to address concerns with excessive bandwidth and storage needs. For example, the JPEG standard provides for block compression and decompression, but typically employs the same compression approach to each block. Such techniques may also permit transfer of certain image data in stages, such as to provide increasingly high resolution as higher frequency components of an image are superimposed on blocks of the image.
However, these techniques have not satisfied the need for very rapid and optimal lossless compression of image data, particularly in medical diagnostic applications. There remains a need, therefore, for improved image data compression techniques capable of offering shortened data processing and transfer times, and reduced memory requirements.
The invention provides an image data compression technique designed to respond to these needs. The technique may be applied in a wide variety of imaging applications, particularly where very large data sets are to be compressed, stored and transferred. The technique is particularly well suited to the medical diagnostic field, in which diagnostic image data is compressed and stored either local to an imaging system, or remotely, such as in a picture archiving and communication system.
The technique provides optimal compression of image data streams by dividing the data streams into subregions. Each subregion encodes pixels of a reconstructed image, such as along a row of the image. The lengths of the subregions may be set to a default value, or may be selected based upon such factors as the imaging system originating the data stream, the features viewable in the image, and so forth. Moreover, the subregions may have different length, while in a present embodiment, all subregions have the same length in terms of pixels encoded. Optimal compression routines or algorithms are analyzed and selected for each subregion. The selection may include a comparison of lengths of compressed data code obtainable through application of a plurality of candidate algorithms. Following selection of the optimal algorithms, each subregion is compressed and the compressed data is assembled to form the compressed image data file